TY - GEN
T1 - Infrastructure state transition probability computation using duration models
AU - Mishalani, Rabi G.
AU - Madanat, Samer M.
PY - 2002
Y1 - 2002
N2 - Sound infrastructure deterioration models are essential for accurately predicting future conditions which, in turn, are key inputs to effective maintenance and rehabilitation decision-making. The challenge central to developing accurate deterioration models is that condition is often measured on a discrete scale, such as inspectors' ratings. Furthermore, deterioration is a stochastic process that varies widely with several factors, many of which are generally not captured by available data. Therefore, probabilistic discrete state models are often used to characterize deterioration. Such models are based on transition probabilities which capture the nature of the evolution of condition states from one time point to the next. However, current methods for determining such probabilities suffer from several serious limitations. An alternative approach addressing these limitations is presented in this paper. A probabilistic model of the time spent in a state is derived and the approach used for estimating its parameters is described. Furthermore, a methodology for determining the corresponding state transition probabilities from the developed duration model is presented. Finally, the overall methodology is demonstrated using a data set of reinforced concrete bridge deck observations.
AB - Sound infrastructure deterioration models are essential for accurately predicting future conditions which, in turn, are key inputs to effective maintenance and rehabilitation decision-making. The challenge central to developing accurate deterioration models is that condition is often measured on a discrete scale, such as inspectors' ratings. Furthermore, deterioration is a stochastic process that varies widely with several factors, many of which are generally not captured by available data. Therefore, probabilistic discrete state models are often used to characterize deterioration. Such models are based on transition probabilities which capture the nature of the evolution of condition states from one time point to the next. However, current methods for determining such probabilities suffer from several serious limitations. An alternative approach addressing these limitations is presented in this paper. A probabilistic model of the time spent in a state is derived and the approach used for estimating its parameters is described. Furthermore, a methodology for determining the corresponding state transition probabilities from the developed duration model is presented. Finally, the overall methodology is demonstrated using a data set of reinforced concrete bridge deck observations.
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U2 - 10.1061/40632(245)64
DO - 10.1061/40632(245)64
M3 - Conference contribution
AN - SCOPUS:0036052730
SN - 0784406324
SN - 9780784406328
T3 - Proceedings of the International Conference on Applications of Advanced Technologies in Transportation Engineering
SP - 505
EP - 512
BT - Proceedings of the International Conference on Applications of Advanced Technologies in Transportation Engineering
PB - ASCE - American Society of Civil Engineers
T2 - Proceedings of the seventh International Conference on: Applications of Advanced Technology in Transportation
Y2 - 5 August 2002 through 7 August 2002
ER -